Big Macs to big data: How dining has changed

Big Macs to big data: How dining has changed

When brothers Dick and Maurice McDonald opened their first restaurant, ‘McDonald's Bar-B-Q’, in 1940, there were 25 items on the menu. By 1948, realising almost all of their money was coming from hamburger sales and not barbeque dishes, they shut down to rethink their business. When they reopened three months later there were just nine items on the menu and a revolutionary self-service window replaced waiters and waitresses – a new era had begun.

The streamlining of the McDonald’s menu and food preparation system is one of the earliest examples of customer insights improving the dining experience and increasing profits. Nearly 80 years later, decisions based on much bigger data sets are shaping every aspect of the food service industry. Not even the enterprising McDonald brothers could have predicted how the fast food model they pioneered would shift in response to advances in automation:

‘Robots will replace humans and cash won’t be accepted at soon-to-be-opened Shake Shack’ is a real news story. In fact, experts have predicted that robots and automation could entirely replace people in the foodservice industry by the mid-2020s.

It’s not just fast food drive-thrus that are becoming increasingly data-driven. Every part of the restaurant industry’s supply chain is seeing improvements thanks to big data and automation. Structured data (collected online and at the point of sale in restaurants) is being combined with unstructured data (including restaurant reviews, social media trends and even weather and traffic reports) to remove logistical inefficiencies, personalise customer experiences, build loyalty among consumers, and ultimately boost profits.

The power of personalisation:

Data can provide a strategic advantage before a restaurant is even built. U.S.-based salad chain Tender Greens learns the best location to open a new outlet using its ‘psychographic’ – a demographic profile of its ideal customer built from customer analytics and refined by local Google search trends.

Once its doors are open, a data-savvy restaurant can then use dozens of smart solutions to improve the dining experience. For example, most of us know the disappointing feeling of a medium steak coming out well-done. Mediterranean restaurant chain Cava used data to tackle the problem, by monitoring grill temperatures with sensors. By avoiding inconsistencies, complaints about food quality dropped by 28%.

The chain, which doubled its U.S. presence in 2017, has also used sensors to analyse queues, finding that customers tend to bunch up around menu boards while making their selection. Instead of removing options, menus were redesigned to let customers know ahead of time what to expect at the serving station, resulting in lines moving 10% faster and holding 12% more customers.

On a larger scale, Google recently announced a forthcoming feature that uses location data to provide live wait times for sit-down restaurants, which means soon we won’t need suffer the annoyance of walking in and then straight out of a packed restaurant.

Then there’s the customer and the need to streamline diners’ experiences and removing the ‘pain points’ caused by frustrating wait times, confusing menus, and clumsy, time-consuming payment methods. Barclaycard is at the forefront of finding a solution.

Dine & Dash:

‘Dine & Dash’ allows you to skip the post-meal wait to pay. Using a totem on the table, customers download the app, then simply tap, eat and go.

Currently being trialled with restaurant chain, Prezzo, Dine & Dash helps improve customer service by speeding up the payment process. It also benefits diners who can split the bill, apply discounts and receive digital receipts.

More choice:

What diners see on menus is no longer the sole choice of the head chef. Restaurant management platform Upserve analysed three years of data from 3,000 restaurants across the U.S. to predict the most crowd-pleasing food items of 2018: cauliflower, coconut and charcoal are all recommended ingredients this year. Other previously popular options, including naan pizza and sauerkraut, are on the way out.

However, menus shouldn’t be decided solely by the bestselling items. Loyalty and guest engagement software makers PayTronix Systems recently found that frequent visitors to food outlets often behave differently to infrequent customers, and that simply removing low-selling items could have a detrimental effect on returning visits. Instead, keeping menu options that appeal to both customer groups is a better strategy for maximising sales.

Understanding sales among large groups is useful, but for truly personalised service, customer behaviour is tracked on an individual level. A small restaurant owner in the past did this by jotting down or remembering the most frequent customers’ preferences and personal details. Now, the most advanced solutions mimic this type of personalisation across thousands or millions of visitors, tracked through apps like OpenTable, which provides customised restaurant recommendations similar to the way Netflix recommends films. When a customer isn’t in the restaurant, communication with them is tailored to feel relevant and enticing – e.g., someone who always orders vegetarian dishes won’t see the offer on chicken wings.

When customers visit once but don’t return, a guest management system like Venga can be used to match reservations to POS data. Dining habits and visit frequencies are crunched and automated email marketing campaigns can then encourage customers to return. Fig & Olive sent ‘we miss you’ emails to guests who had not visited in 30 days alongside an offer of free crostini. It resulted in more than $36,000 in sales – seven times the cost of the campaign itself. The upscale Mediterranean restaurant also sent branded ‘thank you’ emails with feedback surveys to guests the day after their visit, which decreased negative reviews on Yelp by 36%.

Food for thought:

The question currently dominating headlines is whether robotics and automation bring more opportunity than risk to the fast food industry. Silicon Valley pizza-delivery start-up Zume Pizza already automates some of its pizza making, Domino’s has toyed with delivering pizzas by drone, and McDonald’s self-service kiosks are changing the relationship between the service provider and the diner. In response to the concern, the world’s biggest fast-food chain insisted that kiosks will create new jobs in other parts of the restaurant, such as table service.

Elsewhere, big data has been integral to the growth of food delivery giants Deliveroo, Uber Eats and Just Eat. This appetite for online takeaways, especially restaurant-quality options, is still growing, with research firm NPD Group predicting a 17% increase in consumer spending on food delivery by 2019.

With the dining process being streamlined by invisible payments and more businesses combining point of sale and marketing data into actionable knowledge, the refinements to customers’ experiences seem unlimited.

Automation is helping businesses find customers, offer them the perfect meal, deliver or serve it seamlessly, and keep them coming back for more – the only thing it can’t do is eat it for them.